Radar radiation source identification method based on feature fusion

Abstract

The invention relates to a radar radiation source identification method based on feature fusion. The method comprises the steps of generating a radar radiation source unintentional modulation signal set; extracting AR model coefficients, Renyi entropy features and spectral kurtosis features; computing smoothed pseudo Wigner-Ville distribution, generating time-frequency images, and carrying out graying and adaptive binarization to obtain adaptive binarized images; extracting pseudo Zernike matrix and Hu matrix features of the images; extracting signal time-frequency image unintentional modulation features through application of an AlexNet convolutional neural network, carrying out normalization, and carrying out feature fusion to obtain fused feature vectors; and inputting the fused featurevectors into a support vector machine, training the support vector machine optimized through particle swarm optimization, and inputting the radar radiation source signal set into a system to finish identifying radar radiation sources. According to the method, the signals are analyzed from a time domain, a frequency domain and a time-frequency domain, various unintentional modulation features areextracted comprehensively, and the problems that the extracted unintentional modulation features are low applicability and reliability and the radiation sources are difficult to identify is solved.

Description

technical field [0001] The invention relates to a radar radiation source identification method, in particular to a radar radiation source identification method based on feature fusion, which belongs to the field of radar radiation source identification. Background technique [0002] Radar emitter identification is a crucial part of radar information identification, but unintentional modulation identification has become a bottleneck in this field. The unintentional modulation characteristics of radar radiation sources are caused by the imperfection of each physical device of the signal transmitting equipment, and have highly individual attributes. Due to the continuous improvement and progress of radar systems, the traditional single unintentional modulation feature extraction of radar emitters can no longer meet the needs of radar emitter identification. At present, the extraction of unintentional modulation features of radiation sources mainly includes time domain, frequen...

AI Technical Summary

Problems solved by technology

In addition, simply applying the existing technology to extract high-dimensional features of the signal will greatly reduce the computing speed of the classifier, the noise in the transmission and receiving process will directly affect the recognition rate, and the parameter setting of the classifier is too simple to affect the final radiation source identification Effect

Images